AMLD 2025

Workshop: Leveraging Large Language Models for Intelligent Claim Handling: A Hands-On Industry Case Study

Overview

This workshop provides a hands-on exploration of applying Large Language Models (LLMs) to automate and enhance insurance claim handling processes. Participants will learn different approaches to build intelligent claim processing systems using state-of-the-art NLP techniques.

Prerequisites

• Basic understanding of Python programming
• Familiarity with machine learning concepts
• Basic knowledge of natural language processing (NLP)

Agenda

1. Introduction

• Overview of insurance claim processing
• Problem statement and challenges
• Dataset introduction and exploration

2. (Retrieval-Augmented Generation (RAG))[graph_rag/README.md]

• Understanding RAG architecture
• Building a RAG pipeline for claim coverage verification
• Implementation considerations and results analysis

3. Graph-Based Retrieval

• Introduction to graph-based approaches
• Implementation of graph retrieval system
• Performance analysis and considerations

4. Fine-tuning Approach

• Fundamentals of fine-tuning LLMs
• Implementation and best practices
• Results evaluation and comparison

5. Conclusion and Discussion

• Comparative analysis of approaches
• Best practices and lessons learned
• Future directions and improvements